using 
CSV,
Dates,
DataFrames,
Format,
Markdown,
Measures,
Plots,
RCall,
Statistics,
StatsPlots,
Suppressor
@suppress R"library(forecast)"
@suppress gr()
;

death_path = "COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
confirmed_path = "COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
;

death = CSV.read(death_path) |> DataFrame! |> x -> rename!(x, Dict(Symbol("Province/State") => :state, Symbol("Country/Region") => :country))
confirmed = CSV.read(confirmed_path) |> DataFrame! |> x -> rename!(x, Dict(Symbol("Province/State") => :state, Symbol("Country/Region") => :country))
;

"""
    convert an array of columns and column names into a markdown table

    parameters: 
        cols : Array[Array[Any]] | DataFrame
            an array of arrays which are the columns of the table
        names : Array[String]
            an array of column names. If inputting a df, input `string.(names(df))`
        df : Boolean
            whether or not the cols are an array of arrays or a dataframe

    returns: String
        string of markdown text
"""
function markdown_table(cols, names; df=false)
    if df
        cols = [col for col in eachcol(cols)]
    end
    output_string = ""
    cols = [repr.(col) for col in cols]
    for i in 1:length(names)
        output_string = output_string * "|" * names[i]
    end
    output_string *= "\n"
    for i in 1:length(names)
        output_string = output_string * "|" * "---"
    end
    output_string *= "\n"
    for row in 1:length(cols[1])
        for col in 1:length(cols)
            output_string = output_string * "|" * cols[col][row]
        end
        output_string *= "\n"
    end
    # displaying strings without quotes
    output_string =  replace(output_string, "\"" => "")

    return output_string
end
;

function country_data(country; state=false, start_date=Dates.Date(2020, 1, 22), end_date="max")
    aggregate(x) = [sum(col) for col in eachcol(x)]
    
    state_mask = typeof(state) == String ? death.state .== state : true
    d = death[(death.country .== country) .& state_mask, 5:end] |> aggregate
    dates = [Dates.Date(2020, 1, 22) + Dates.Day(day) for day in 1:length(d)] |> x -> reshape(x, :, 1)
    state_mask = typeof(state) == String ? confirmed.state .== state : true
    c = confirmed[(confirmed.country .== country) .& state_mask, 5:end] |> aggregate
    
    df = convert(DataFrame, dates) |> x -> rename!(x, [:date])
    country_name = typeof(state) == String ? country * "-" * state : country
    df[!, :country] .= country_name
    df[!, :confirmed] = c
    df[!, :deaths] = d
    df[!, :death_rate] = d ./ c
    df[!, :new_cases] .= 0
    df[2:end, :new_cases] = df[2:end, :confirmed] .- df[1:end-1, :confirmed]
    df[!, :new_deaths] .= 0
    df[2:end, :new_deaths] = df[2:end, :deaths] .- df[1:end-1, :deaths]
    df[!, :acceleration_cases] .= 0
    df[2:end, :acceleration_cases] = df[2:end, :new_cases] .- df[1:end-1, :new_cases]
    df[!, :acceleration_deaths] .= 0
    df[2:end, :acceleration_deaths] = df[2:end, :new_deaths] .- df[1:end-1, :new_deaths]
    df[!, :days_since_100] .= 0
    counter = 0
    for row in eachrow(df)
        if row.confirmed > 100
            counter += 1
        end
        row.days_since_100 = counter
    end
    df[!, :days_since_10] .= 0
    counter = 0
    for row in eachrow(df)
        if row.deaths > 10
            counter += 1
        end
        row.days_since_10 = counter
    end
    
    if end_date == "max"
        end_date = maximum(df.date)
    end
    return df[(df.date .>= start_date) .& (df.date .<= end_date), :]
end
;

function plot_country(country; state=false, metric=:confirmed, start_date=Dates.Date(2020, 1, 22), end_date="max", days_since_100=false)
    data = country_data(country; state=state, start_date=start_date, end_date=end_date)
    if days_since_100
        if metric == :confirmed
            plot(data[data.days_since_100 .> 0, :days_since_100], data[data.days_since_100 .> 0, metric], label=country, legend=:outertopright, size=(1000, 500))
        else
            plot(data[data.days_since_10 .> 0, :days_since_10], data[data.days_since_10 .> 0, metric], label=country, legend=:outertopright, size=(1000, 500))
        end
    else
        plot(data.date, data[!, metric], label=country, legend=:outertopright, size=(1000, 500))
    end
    
    plot!([0], linetype=:hline, color=:black, label="")
    plot!(yformatter=:plain)
end
function plot_country!(country; state=false, metric=:confirmed, start_date=Dates.Date(2020, 1, 22), end_date="max", days_since_100=false)
    data = country_data(country; state=state, start_date=start_date, end_date=end_date)
    if days_since_100
        if metric == :confirmed
            plot!(data[data.days_since_100 .> 0, :days_since_100], data[data.days_since_100 .> 0, metric], label=country, legend=:outertopright, size=(1000, 500))
        else
            plot!(data[data.days_since_10 .> 0, :days_since_10], data[data.days_since_10 .> 0, metric], label=country, legend=:outertopright, size=(1000, 500))
        end
    else
        plot!(data.date, data[!, metric], label=country, legend=:outertopright, size=(1000, 500))
    end
    plot!(yformatter=:plain)
end
bar_plot(country, metric) = bar(
    all_country_data[
        (all_country_data.country .== country) .& (all_country_data.date .>= Dates.Date(2020, 1, 1)),
        :date
    ], 
    all_country_data[
        (all_country_data.country .== country) .& (all_country_data.date .>= Dates.Date(2020, 1, 1)),
        metric
    ], 
    legend=false, 
    linecolor=1, 
    title=country,
    yformatter=:plain
)
function accel_plot(country_name="World")
    if country_name == "World"
        tmp = all_country_data
    else
        tmp = all_country_data[all_country_data.country .== country_name, :]
    end
    agg = by(tmp, [:date], :confirmed => sum, :deaths => sum)
    conf = plot(agg.date, agg.confirmed_sum, legend=false, title="$country_name Confirmed Cases", size=(1400, 700), color=:grey, yformatter=:plain)
    death_plot = plot(agg.date, agg.deaths_sum, legend=false, title="$country_name Deaths", size=(1400, 700), color=:orange, yformatter=:plain)

    # derivates
    agg[!, :new_cases] .= 0
    agg[!, :new_cases][2:end] = agg.confirmed_sum[2:end] - agg.confirmed_sum[1:end-1]
    agg[!, :new_deaths] .= 0
    agg[!, :new_deaths][2:end] = agg.deaths_sum[2:end] - agg.deaths_sum[1:end-1]
    agg[!, :conf_acceleration] .= 0
    agg[!, :conf_acceleration][2:end] = agg.new_cases[2:end] - agg.new_cases[1:end-1]
    agg[!, :death_acceleration] .= 0
    agg[!, :death_acceleration][2:end] = agg.new_deaths[2:end] - agg.new_deaths[1:end-1]

    # moving average
    moving_average(vs,n) = [sum(@view vs[i:(i+n-1)])/n for i in 1:(length(vs)-(n-1))]
    n_days = 3
    ma_date = agg.date[1 + n_days-1:end]
    conf_slope = moving_average(agg.new_cases, n_days)
    death_slope = moving_average(agg.new_deaths, n_days)
    conf_acc = moving_average(agg.conf_acceleration, n_days)
    death_acc = moving_average(agg.death_acceleration, n_days)

    p_conf_slope = plot(ma_date, conf_slope, legend=false, title="$country_name 1st Derivative (new cases)", size=(1400, 700), color=:grey)
    plot!([0], linetype=:hline, color=:black, label="", yformatter=:plain)

    p_death_slope = plot(ma_date, death_slope, legend=false, title="$country_name 1st Derivative (new deaths)", size=(1400, 700), color=:orange)
    plot!([0], linetype=:hline, color=:black, label="", yformatter=:plain)

    p_conf_acc = plot(ma_date, conf_acc, legend=false, title="$country_name 2nd Derivative (cases acceleration)", size=(1400, 700), color=:grey)
    plot!([0], linetype=:hline, color=:black, label="", yformatter=:plain)

    p_death_acc = plot(ma_date, death_acc, legend=false, title="$country_name 2nd Derivative (deaths acceleration)", size=(1400, 700), color=:orange)
    plot!([0], linetype=:hline, color=:black, label="", yformatter=:plain)

    plot(conf, death_plot, p_conf_slope, p_death_slope, p_conf_acc, p_death_acc, layout=(3,2))
end
;

function r_forecast(x, y; r_forecast_function, time_function, h)    
    fc_x = [x[end] + time_function(i) for i in 1:h]
    fc = r_forecast_function(y)
    point_forecast = [val for val in fc[2]]
    lo_80 = [val for val in fc[6][1:h]]
    hi_80 = [val for val in fc[5][1:h]]
    lo_95 = [val for val in fc[6][h+1:h*2]]
    hi_95 = [val for val in fc[5][h+1:h*2]]
    output = hcat([point_forecast, lo_80, hi_80, lo_95, hi_95]...) |> DataFrame |> x -> rename!(x, [:point_forecast, :lo_80, :hi_80, :lo_95, :hi_95])

    p = plot(x, y, legend=false, label="Observed", color=:black, formatter=:plain)
    plot!(fc_x, point_forecast, ribbon=(point_forecast.-lo_95, hi_95.-point_forecast), label="", color=:lightblue, fillalpha=0.5)
    plot!(fc_x, point_forecast, ribbon=(point_forecast.-lo_80, hi_80.-point_forecast), label="Forecast", color=:blue, fillalpha=0.3)
    
    return [fc, output, p]
end
;

function individual_country(country; comparison_country="Italy")
    # line plots
    cases_100 = plot_country(country, days_since_100=true, metric=:confirmed)
    plot_country!(comparison_country, days_since_100=true, metric=:confirmed)
    plot!(title="$country Days Since 100th Case", titlefont=font(10))
    deaths_10 = plot_country(country, days_since_100=true, metric=:deaths)
    plot_country!(comparison_country, days_since_100=true, metric=:deaths)
    plot!(title="$country Days Since 10th Death", titlefont=font(10))
    line_plots = plot(cases_100, deaths_10, layout=(2,1))
    
    #accel plots
    accel_plots = accel_plot(country)
    plot!(titlefont=font(10))
    
    # bar plots
    cases_bar = bar_plot(country, :new_cases)
    plot!(title="$country New Cases Per Day", titlefont=font(10))
    deaths_bar = bar_plot(country, :new_deaths)
    plot!(title="$country Deaths Per Day", titlefont=font(10))
    bar_plots = plot(cases_bar, deaths_bar, layout=(2,1))
    
    # forecast plots
    data = country_data(country) |> x -> x[x.confirmed .>= 100,:]
    forecast_plot_cases = r_forecast(data.date, data.confirmed, r_forecast_function = z -> R"holt"(z, h=20, damped=true), time_function=Dates.Day, h=20)[3]
    plot!(title="$country Forecast Cases", titlefont=font(10))
    forecast_plot_deaths = r_forecast(data.date, data.deaths, r_forecast_function = z -> R"holt"(z, h=20, damped=true), time_function=Dates.Day, h=20)[3]
    plot!(title="$country Forecast Deaths", titlefont=font(10))
    forecast_plots = plot(forecast_plot_cases, forecast_plot_deaths, layout=(1, 2))
    
    final_plot = plot(accel_plots, line_plots, bar_plots, forecast_plots, layout=(4, 1))
    plot!(size=(900, 2000), margin=5mm)
    return final_plot
end
;

all_countries = death.country |> unique |> sort
all_country_data = vcat([country_data(country) for country in all_countries]...)
current_state = by(all_country_data, [:country], :confirmed => maximum, :deaths => maximum)
current_state[!, :death_rate] = current_state.deaths_maximum ./ current_state.confirmed_maximum
;

Markdown.parse("""
    # Overview of COVID-19 as of $(maximum(all_country_data.date))
    This page uses data from https://github.com/CSSEGISandData/COVID-19 (Johns Hopkins CSSE is the original source) to create a report on the status of COVID-19 cases and deaths around the world. It is updated once a day around 10:00am CET.
    """)

Worldwide Current State¶

tmp = current_state
Markdown.parse("""
- Confirmed cases: **$(format(sum(tmp.confirmed_maximum), commas=true))**
- Deaths: **$(format(sum(tmp.deaths_maximum), commas=true))**
- Death Rate: **$(round(sum(tmp.deaths_maximum) / sum(tmp.confirmed_maximum) * 100, digits=2))%**
""")

accel_plot("World")
plot!(size=(900, 700))

Top 10 Worst Affected Countries¶

Confirmed¶

top_10 = sort(current_state, order(:confirmed_maximum, rev=true))[1:10, :]
top_10[!, :death_rate] = string.(round.(top_10.death_rate * 100, digits=2)) .* "%"
Markdown.parse(
    markdown_table(top_10, ["Country", "Confirmed Cases", "Deaths", "Death Rate"]; df=true)
)

Acceleration (>1000 cases now)¶

The Acceleration of Last 5 Days column is calculated by the average second derivative over the last 5 days / number of cases 5 days ago. It doesn't have much intrinsic meaning but is rather a more comparable/relative measure between countries of how fast new cases are accelerating.

countries = unique(all_country_data.country)
acceleration = []
cases_5_ago = []
cases_now = []
for country in countries
    metric = mean(all_country_data[all_country_data.country .== country, :acceleration_cases][end-4:end])
    metric /= all_country_data[all_country_data.country .== country, :confirmed][end-4]
    if isnan(metric)
        metric = 0
    end
    push!(acceleration, metric)
    push!(cases_5_ago, all_country_data[all_country_data.country .== country, :confirmed][end-4])
    push!(cases_now, all_country_data[all_country_data.country .== country, :confirmed][end])
end
last_5 = rename!(DataFrame([countries, acceleration, cases_now, cases_5_ago]), [:country, :last_5_accel, :cases_now, :cases_5_ago])
last_5[!, :perc_increase] = last_5.cases_now ./ last_5.cases_5_ago .- 1
tmp = sort(last_5[(last_5.cases_5_ago .> 20) .& (last_5.cases_now .> 1000), :], order(:last_5_accel, rev=true))
tmp[!, :perc_increase] = string.(round.(tmp.perc_increase .* 100, digits=2)) .* "%"
rename!(tmp, [:Country, Symbol("Acceleration of Last 5 Days"), Symbol("Cases Now"), Symbol("Cases 5 Days Ago"), Symbol("% Increase in 5 Days")])
tmp[!, Symbol("Acceleration of Last 5 Days")] = string.(round.(tmp[!, Symbol("Acceleration of Last 5 Days")] * 100, digits=2)) .* "%"

Markdown.parse(
    markdown_table(tmp, string.(names(tmp)), df=true)
)

Countries¶

Days since 100th case and 10th death plotted with Italy for comparison. Only countries with >= 500 confirmed cases show graphs. Derivative plots are 3 days moving average.

Forecasts¶

Forecast numbers and plots are created using Holt's linear trend method with dampening. Important to note is that this is just one of many methods that can forecast the data, and I have not spent a significant amount of time validating it. I may spend more time in the future investigating better forecasting methods.
Forecast numbers are given for 10 and 20 days out with the 80% prediction interval. Plots display the point forecast and 80% prediction interval (dark blue shading), and 95% prediction interval (light blue shading).

for coun in all_countries
    data = country_data(coun)
    display(Markdown.parse("## $coun"))
    Markdown.parse("""
- Confirmed cases: **$(format(maximum(data.confirmed), commas=true))**
- Deaths: **$(format(maximum(data.deaths), commas=true))**
- Death Rate: **$(round(maximum(data.deaths) / maximum(data.confirmed) * 100, digits=2))%**
""") |> display
    if maximum(data.confirmed) >= 500 || coun == "Hungary" 
        h = 20
        raw_fc_data = data[data.confirmed .>= 100, :]
        fc_data_cases = r_forecast(raw_fc_data.date, raw_fc_data.confirmed, r_forecast_function = z -> R"holt"(z, h=h, damped=true), time_function=Dates.Day, h=h)[2]
        fc_data_cases = round.(fc_data_cases, digits=0)
        fc_data_deaths = r_forecast(raw_fc_data.date, raw_fc_data.deaths, r_forecast_function = z -> R"holt"(z, h=h, damped=true), time_function=Dates.Day, h=h)[2]
        fc_data_deaths = round.(fc_data_deaths, digits=0)
        _10_days = maximum(data.date) + Dates.Day(10)
        _20_days = maximum(data.date) + Dates.Day(20)
        Markdown.parse("""
**$coun Forecast**
<br>
_By $(_10_days)_
- Confirmed cases: **$(format(fc_data_cases[10, :point_forecast], commas=true))** (+/- **$(format(fc_data_cases[10, :point_forecast] - fc_data_cases[10, :lo_80], commas=true))**)
- Deaths: **$(format(fc_data_deaths[10, :point_forecast], commas=true))** (+/- **$(format(fc_data_deaths[10, :point_forecast] - fc_data_deaths[10, :lo_80], commas=true))**)
_By $(_20_days)_
- Confirmed cases: **$(format(fc_data_cases[20, :point_forecast], commas=true))** (+/- **$(format(fc_data_cases[20, :point_forecast] - fc_data_cases[20, :lo_80], commas=true))**)
- Deaths: **$(format(fc_data_deaths[20, :point_forecast], commas=true))** (+/- **$(format(fc_data_deaths[20, :point_forecast] - fc_data_deaths[20, :lo_80], commas=true))**)
""") |> display
        display(individual_country(coun))
    end
end

┌ Warning: RCall.jl: Warning in ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped,  :
│   Not enough data to use damping
└ @ RCall /Users/danielhopp/.julia/packages/RCall/g7dhB/src/io.jl:113
┌ Warning: RCall.jl: Warning in ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped,  :
│   Not enough data to use damping
└ @ RCall /Users/danielhopp/.julia/packages/RCall/g7dhB/src/io.jl:113
┌ Warning: RCall.jl: Warning in ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped,  :
│   Not enough data to use damping
└ @ RCall /Users/danielhopp/.julia/packages/RCall/g7dhB/src/io.jl:113
┌ Warning: RCall.jl: Warning in ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped,  :
│   Not enough data to use damping
└ @ RCall /Users/danielhopp/.julia/packages/RCall/g7dhB/src/io.jl:113

Country	Confirmed Cases	Deaths	Death Rate
US	243453	5926	2.43%
Italy	115242	13915	12.07%
Spain	112065	10348	9.23%
Germany	84794	1107	1.31%
China	82432	3322	4.03%
France	59929	5398	9.01%
Iran	50468	3160	6.26%
United Kingdom	34173	2926	8.56%
Switzerland	18827	536	2.85%
Turkey	18135	356	1.96%

Country	Acceleration of Last 5 Days	Cases Now	Cases 5 Days Ago	% Increase in 5 Days
India	8.69%	2543	1024	148.34%
Russia	7.08%	3548	1534	131.29%
United Arab Emirates	5.16%	1024	570	79.65%
Brazil	3.39%	8044	4256	89.0%
Canada	2.64%	11284	6280	79.68%
Pakistan	2.27%	2421	1597	51.6%
Ecuador	1.94%	3163	1924	64.4%
United Kingdom	1.76%	34173	19780	72.77%
Turkey	1.63%	18135	9217	96.76%
Poland	1.54%	2946	1862	58.22%
US	1.46%	243453	140886	72.8%
Sweden	1.31%	5568	3700	50.49%
Romania	1.3%	2738	1815	50.85%
Peru	1.29%	1414	852	65.96%
Saudi Arabia	1.02%	1885	1299	45.11%
South Africa	1.02%	1462	1280	14.22%
Japan	0.99%	2495	1866	33.71%
Denmark	0.91%	3573	2564	39.35%
Israel	0.85%	6857	4247	61.46%
Ireland	0.83%	3849	2615	47.19%
Colombia	0.77%	1161	702	65.38%
Mexico	0.73%	1378	848	62.5%
Philippines	0.71%	2633	1418	85.68%
Chile	0.69%	3404	2139	59.14%
Greece	0.59%	1544	1156	33.56%
Panama	0.53%	1317	901	46.17%
Iceland	0.51%	1319	1020	29.31%
Malaysia	0.4%	3116	2470	26.15%
Norway	0.11%	5147	4284	20.14%
Spain	0.11%	112065	80110	39.89%
Indonesia	0.06%	1790	1285	39.3%
Germany	0.03%	84794	62095	36.56%
China	-0.01%	82432	82122	0.38%
Czechia	-0.01%	3858	2817	36.95%
Thailand	-0.07%	1875	1388	35.09%
Iran	-0.1%	50468	38309	31.74%
Korea, South	-0.12%	9976	9583	4.1%
Switzerland	-0.12%	18827	14829	26.96%
Netherlands	-0.15%	14788	10930	35.3%
Italy	-0.27%	115242	97689	17.97%
Portugal	-0.4%	9034	5962	51.53%
Austria	-0.45%	11129	8788	26.64%
Singapore	-0.5%	1049	844	24.29%
Argentina	-0.59%	1133	745	52.08%
Luxembourg	-0.59%	2487	1950	27.54%
Croatia	-0.65%	1011	713	41.8%
Belgium	-0.86%	15348	10836	41.64%
Finland	-0.87%	1518	1240	22.42%
Dominican Republic	-0.98%	1380	859	60.65%
Australia	-1.22%	5116	3984	28.41%
France	-1.24%	59929	40708	47.22%
Serbia	-2.46%	1171	741	58.03%

Overview of COVID-19 as of 2020-04-03¶

Worldwide Current State¶

Top 10 Worst Affected Countries¶

Confirmed¶

Acceleration (>1000 cases now)¶

Countries¶

Forecasts¶

Afghanistan¶

Albania¶

Algeria¶

Andorra¶

Angola¶

Antigua and Barbuda¶

Argentina¶

Armenia¶

Australia¶

Austria¶

Azerbaijan¶

Bahamas¶

Bahrain¶

Bangladesh¶

Barbados¶

Belarus¶

Belgium¶

Belize¶

Benin¶

Bhutan¶

Bolivia¶

Bosnia and Herzegovina¶

Botswana¶

Brazil¶

Brunei¶

Bulgaria¶

Burkina Faso¶

Burma¶

Burundi¶

Cabo Verde¶

Cambodia¶

Cameroon¶

Canada¶

Central African Republic¶

Chad¶

Chile¶

China¶

Colombia¶

Congo (Brazzaville)¶

Congo (Kinshasa)¶

Costa Rica¶

Cote d'Ivoire¶

Croatia¶

Cuba¶

Cyprus¶

Czechia¶

Denmark¶

Diamond Princess¶

Djibouti¶

Dominica¶

Dominican Republic¶

Ecuador¶

Egypt¶

El Salvador¶

Equatorial Guinea¶

Eritrea¶

Estonia¶

Eswatini¶

Ethiopia¶

Fiji¶

Finland¶

France¶

Gabon¶

Gambia¶

Georgia¶

Germany¶

Ghana¶

Greece¶

Grenada¶

Guatemala¶

Guinea¶

Guinea-Bissau¶

Guyana¶